Data Analysis Project:

For this project, imagine that you are a decision maker and need to analyze multiple

alternatives (at least 3). For this project, you must collect dataset(s) that include at least 2

qualitative variables and 4 quantitative variables.

Below is a list of requirements for the project:

1. Create a Purpose slide that discusses the who?, what?, when?, why? Note that although

you have flexibility to choose an area of interest, it should be appropriately applied to

real-world decisions in fields such as sports, business, financial analysis, etc.

2. One of the most important aspects of decision making is understanding the data.

Create appropriate visualization tools, and include a slide for each that highlights it’s

utilization and value-add. Make sure to identify the type of data being analyzed and

ensure that appropriate statistics and visualizations are being provided.

o

You must have a have a histogram (including discussion of symmetry,

skewness, etc.), box-and-whisker plot (comparing multiple alternatives), and

scatter plot (correlation analysis for two variables for each alternative).

3. Use appropriate statistical measures to help decision makers analyze variables and/or

alternatives and make informed decisions.

o

You must include descriptive statistics such as mean, trimmed mean, median,

mode, range, variance, standard deviation, quartiles, interquartile range, and

coefficient of variation. All of these are available in Minitab – Stat – Display

Descriptive Statistics – Statistics.

o

You must determine if the dataset(s) are resistant.

o

You must determine any outliers that exist in your dataset(s) and discuss why

they may exist, and what you feel is appropriate to do with them.

o

You must find the correlation coefficient between two variables in your

dataset(s) for each of the alternatives.

o

If you have time series data, then you must compute a 2 and 3 period moving

average. In Minitab, use Stat – Time Series – Moving Average, see the

hyperlink for additional information. Enter your data for Moving Average Minitab

o

If you have grouped data, then you must find the mean and standard deviations.

o

You may also considering including, but not required, empirical rule concepts,

measures of relative position, data subsetting, and proportions.

o

You may also consider including, but not required, other data visualizations

discussed throughout the course.

4. Your project should analyze multiple variables and alternatives. You must have at least

2 qualitative variables, 4 quantitative variables, and 3 alternatives, but you are

welcome to include as may as you feel is needed for your given decision problem.

Some examples that you may consider:

â€¢

Comparing various sports teams/players/etc.

â€¢

Stock market analysis. Yahoo Finance also includes data on cryptocurrencies, so it

may be interesting to compare various portfolio options and think about the type of

investor (e.g. conservative, moderate, aggressive) that may be appropriate for each.

â€¢

Comparing multiple Universities/vehicles/etc. and selecting the best alternative.

Of course, you have the flexibility to pursue your own area of interest! If you want to discuss

a project topic proposal with me, please send me an email.

I created an Up Post rating scheme so you can “thumbs up” projects, students responses, etc.

that you feel were top tier and helpful! This is a great way to direct students to value adding

areas within the discussion board which will further support everyone’s learning!

I also selected a setting that requires you to create your own thread before you can read and

reply to other student’s posts. The purpose of this is for everyone to work on their own project

before seeing products produced by classmates.

Please save your work as LastName_FirstName_Project.

Assignment 2

Department of Computer Science and Engineering

Faculty of Engineering

University of North Texas

CSCE5390 sections 001/002

Summer 2022

Due on or before July 22, 2022

This assignment is on computing motion vectors for a given video. Please follow the

following steps.

1. Capture a one-minute video. (A hallway video will give you the best result)

2. Use ffmpeg tool to extract individual frames. (Refer to the documentation on how to

do this, this is a part of the assignment)

3. Use 16×16 blocks to compute MVs. You can use sequential search.

4. Search area size can be varying, and the student must come up with a best value for

P.

5. Compute the MVs using your language of your choice. MATLAB is preferred as it will

lift most of the heavy weight.

6. Create a CSV (comma separated values) file for each pair of frames with the

following information

Block number

Current frame

X

Y

Previous frame

U

V

The header is present in the above table to make it clear for you. The CSV file you

may create can have five columns without any header that will make it easy for your

to do the programming part. Such that the final file will have values as given below,

and f6

1, 0,0, 4, 3

2,16,0, 18, 4

Submit your motion vector files as a compressed file that includes all the MV CSV

files generated for each pair of the frames (name the file with the previous frame

number: ex: 1.csv, 3.csv, etc.). We may ask you to provide the video captured by you

at any time and keep it handy. You do not need to submit the video.

Create a simple word document and include one pair of frames from your video and

the first 10 entries of the MV csv file that was generated for the given pair of frames.

Include a snapshot of your MV calculation program in the word document.

Include steps on how to run the program so that we can test it locally and grade it

accordingly.

How To Improve The

Chicago Bears Oï¬€ense

In The NFL draft

(based on 2021-2022 season)

James Leonard

Purpose

â—

The Chicago Bears have not had a superbowl win since 1985. The team last appeared in a

superbowl in 2007 and have not had a playoff win since 2011.

â—

Being one of the largest cities in the nation and also the oldest NFL team, the Chicago Bears

market is very large and can get even larger if the team starts winning games.

â—

One of the biggest issues with the team has always been its offensive production, often

ranking anywhere in the bottom half of the league to the bottom quarter.

â—

The goal of this research is to take a look at the teams biggest competition and see what

positions they can draft at with its 39th overall draft pick to boost offensive production. The

three positions, or alternatives being closely examined will be Receiver, Running Back and

Offensive Lineman. A quarterback was drafted with the ï¬rst pick last season so that will not

be measured.

Season Stats Compared To Major Opponents

â—

â—

â—

One data set that will be examined to

assist in decision making of what

position to draft using the #39 pick is

the team’s offensive stats compared to

the teams major opponents.

Minnesota (MIN), Green Bay (GB),

Detroit (DET) are within the Bears

division (NFC North), so the team plays

them each twice per season which is

nearly a third of its total games.

Winning the division is a very important

step in making the playoffs and

eventually winning the Super Bowl. The

Los Angeles (LAR) was the best team in

the NFL and won the superbowl this

season.

Lastly, is the NFL average of these stats

to compare the Bears to the rest of the

league.

Passing

â—

â—

â—

By using a Histogram, the

frequency at which each

sample attains each stat can

be observed easily.

This histogram is

symmetrical and exactly

three of the samples had

under 4,250 yards total and

three of the six had over

that amount.

By looking at the total

passing yardage, one can

observe that the Chicago

Bears can use some help in

the passing game. By using

this data to make further

inferences into the

passing-game issues of the

Bears it can be determined

whether or not the team

should draft some sort of

receiver.

Receiving

â—

â—

â—

The two datasets of

Pass attempts and

yardage were chosen

to be compared in

order to see how

efï¬cient each offense

is.

The data

demonstrates a

positive correlation

between the amount

of pass attempts and

touchdowns. But

there is one outlier in

this set but it is not

signiï¬cant enough to

make it non-resistant.

Without the Bears X

and Y The Mean of X

would change from

29.3 to 29.83 and the

Mean of Y would

change from 587.33

to 596.4

Statistics

Receiving Yards – Bears are last in this category being 577 less than the mean average.

Receiving Touchdowns – Bears are last in this category being 13.83 below the mean average.

Pass Attempts – The Bears are also last in this category being 45.33 below the mean average.

Stats

Relative Frequencies – (frequency of event / total number of events)

The probability of a team having less than 4,000 passing yards = 3/6 = .5 = 50% chance

The probability of a team having over 3,635 passing yards = 5/6 = .8333 = 83% chance

The probability of a team having less than 20 touchdowns with over 500 passing attempts

is 1/6 =.167 = 16.7% chance

This data can help in determining goals for the future of the team based on this years

stats. It tells them that they are way below the average passing yardage, passing

touchdowns and attempts which means some improvement at the receiver position is

needed.

Solutions

Correlation Coefï¬cients between passing attempts and touchdowns based on scatterplot X values: 16 34 39 23 41 26 Y values: 542 604 593 593 607 585 X Mean: 29.833 Y Mean: 2789.333

(X – Mx)(Y – My) = Product of Deviation Scores (627.111 + 69.444 +51.944 + -38.722 + 219.611 +

8.944) = 938.333

(Y – My) ^2 = Y Sum of Squares = 2055.111 + 277.778 + 32.111 + 32.111 + 386.778 + 5.444 =

2789.333

(X – Mx) ^2 = X Sum of Squares = 191.361 + 17.361 + 84.028 + 46.694 + 124.694 + 14.694 = 478.833

Hence, r = 938.333 Product of Deviation Scores / âˆš((478.833 Sum of Squares – x values)(2789.333

Sum of Squares Y values)) = .8119

Therefore, the relationship between passing attempts and touchdowns has a strong positive

correlation because .8119 is greater than .7 and is positive. This can tell the team that they need to

increase its passing attempts to therefore score more passing touchdowns and will hopefully result in

outsourcing more teams and winning more games.

Rushing

â—

â—

â—

In this histogram the

rams are the

obvious outlier with

1,683.

The Bears, lead this

category with 2,018

rushing yards.

This data is

particularly

interesting because

the Rams won

Superbowl LVI, yet

they are by far dead

last in this category.

Rush Att.

â—

â—

â—

â—

â—

The two datasets of Pass

attempts and yardage were

chosen to be compared in

order to see how efï¬cient

each offenses running

productivity is.

In this case, productivity is

counted in scoring

successes.

Based on this graph, a

positive correlation can be

seen between the amount of

rushing attempts by each

team and its total number of

rushing touchdowns. The

bears are an outlier in terms

of attempts, but are only just

in the top third of the data in

touchdowns.

This data is resistant

because there is no

signiï¬cant difference

between stats that would

change the mean.

If the Bears, with 26 more

attempts than the next, at

49.

Statistics

Rushing Yardage- The Bears lead this category with 2018 (122 above the mean average)

Rushing Touchdowns – The Bears are second in this category with 14 (1.5 above mean average)

Rushing Attempts – The Bears lead this category with 475 (30 above mean average)

Equations

Relative Frequencies – (frequency of event / total number of events)

The probability of a team having over 2,000 rushing yards is 1/6 =.167 = 16.7% chance

The probability of a team having over 1,500 rushing yards on more than 450 attempts

is 2/6 = .333 = 33% chance

The probability of a team having less than 1900 yards on less than 430 attempts is 2/6

= .333 = 33% chance

The Bears lead the rushing yardage category nearly 60 yards above the NFL average,

but are two touchdowns below the NFL average. All in all, a determination that the

Bears have a solid running game is logical. The team has one more touchdown and 118

yards more than its division winner (GB) and nearly 400 more rushing yards and 4

more touchdowns than Super Bowl LVI winner (LAR)

Equations

Correlation Coefï¬cient – Between rushing attempts and touchdowns based on scatterplot

X values: 14, 10, 13, 12, 10, 16Y values: 475, 449, 446, 427, 420, 453 X Mean: 12.5 Y Mean:

445

(X – Mx)(Y – My) = Product of Deviation Scores (45.000 -10.000 + .500 + 9 + 62.5 + 280 = 135

(Y – My) ^2 = Y Sum of Squares = 900 + 16 + 1 + 324 + 625 + 64 = 1930

(X – Mx) ^2 = X Sum of Squares = 2.250 + 6.250 +.250 + .250 + 6.25 + 12.25 = 27.5

Hence, r = 135 Product of Deviation Scores / âˆš(27.5 Sum of Squares – x values)(1930 Sum of

Squares Y values)) = 0.586

Therefore, the correlation between rushing attempts is positive, but moderate at .586. It is less

than 7, but still close enough to it that it is moderate and not weak. Based on this one can see

that more rushing attempts mean more touchdowns and in the Bears case they are ahead or in

the top percentile of both of those categories.

O-Line

â—

â—

â—

Sacks can be

detrimental in holding

back an offense

production.

The Bears are the

obvious outlier in this

category with 58 sacks.

The next closest data

point is Detroit at 36.

This data is resistant

because when the Bears

data is excluded the

mean average changes

from 37.6 to 33.6. This

is not skewed and the

one outlier does not

change the data set by

too much.

Stats

Sacks Allowed – The Bears lead this category with 58 (20.33 above the mean average)

By examining this data it becomes apparent that the Bears do have an issue on the offensive

line.

The issue than becomes which stats can be used to ï¬nd if there is a correlation between sacks

and offensive production?

The offensive line not only products the quarterback while they are passing, but also creates

holes for a running back to rush through. The bears are last in Passing yards, but ï¬rst in

rushing.

O-Line

â—

â—

To ï¬nd a correlation

between the number

of sacks and its

impact on offensive

production, the total

offensive yardage of

each team will be

used in a scatterplot.

This data will be

conceived by adding

each teams total

rushing yardage with

its total passing

yardage.

Solutions

Relative Frequencies – (frequency of event / total number of events)

The probability of a team having over 30 sacks and over 6,000 total offensive

yards = 2/6 = .333 = 33% chance

The probability of a team having under 30 total sacks is 0%

The probability of having over 7,000 yards of total offense is 1/6 = 16.7%

Despite the Bears having the most total sacks by a team in the entire league,

they still managed to have the most total yardage out of their major competitors

and were almost 2,000 yards above league average (1,813 total). Most of this

comes from the teams high run volume, but the team is getting some signiï¬cant

protection in order to come up with these numbers. That, or the offensive

playmakers are extremely skilled.

Solutions

Correlation Coefï¬cient – Between total sacks allowed and total yardage based on scatterplot

X values: 58, 30, 33, 36, 31, 38 Y values: 7653, 6380, 6426, 5770, 6576, 5840 X Mean: 37.667 Y Mean:

6440.833

(X – Mx)(Y – My) = Product of Deviation Scores (24647.389 + 466.389 + 69.222 + 1118.056 -901.111 -200.278

= 25199.667

(Y – My) ^2 = Y Sum of Squares = 1469348.028 + 3700.694 + 220.028 + 450017.361 + 18270.028 +

361000.694 = 2302556.833

(X – Mx) ^2 = X Sum of Squares = 413.444 + 58.778 + 21.778 + 2.778 + 44.444 + .111 = 541.333

Hence, r = 25199.667 Product of Deviation Scores / âˆš( 541.33 Sum of Squares – x values)(2302556.833 Sum of

Squares Y values)) = 0.7138

Therefore, according to the data there is a moderately positive correlation, but this doesnâ€™t necessarily make

sense as a football analyst since sacks mean a loss of yardage on the play, but this equation states that more lost

yardage equals more total offensive yardage production, which leads me to believe that the correlation may be

weak. Despite this, because of the Bears total yards compared to other teams the O-Line seems to be doing well

based off that statistic alone.

Wins

â—

â—

â—

The data in this

Histogram is

spread out

relatively evenly.

The Bears are in

the lower third of

the data by two

wins.

The data is

resistant because

there are no

outliers or skewed

points.

Box Plot

â—

â—

â—

â—

â—

The goal of this boxplot is to

compare the three alternatives of

Receiver, Running Back and O-Line

needs by looking at all of the data

from each category side by side.

For passing total yardage is used,

for Rushing total rushing yards is

used and for O-line total sacks

allowed is used.

The Bears Passing game is

obviously at the bottom, but so is

the total sacks allowed. The

Offensive line allowing that many

sacks is detrimental to offensive

production.

Buying time for the quarterback to

be able to make throws is essential

despite providing some good

protection.

However, there may be some

qualitative data that can help

explain the passing issues.

Qualitative Data

Most Common Play Call

Chi – Pass

MIN – Pass

GB – Pass

DET – Pass

LAR – Pass

NFL – Pass

Time Of Possession Vs.

Opponent

Chi – Less

MIN – More

GB – Less

DET – More

LAR – More

NFL – Less

Result

There is no correlation between what a teams

most common play type is and the amount of

possession they have the ball for.

The time per possession also does not

determine a teams success based on the

quantitative data. However, all of the teams

who had more possession time aside from

Green Bay were able to have more possession

than its opponent per game. Therefore, from

this data it can be determined that more

successful passing attempts lead to longer

possession times which can lead to putting

more stress on the other teams offensive

efï¬ciency.

At the end of the day, this basically means that

being able to hold the ball longer gives

opponents less time to score.

Passing the ball stops the clock more often than

running it and the majority of teams that have

the ball for more time pass more than run.

Final Decision

â— Based on the ï¬ndings in this study, both teams that won the Super

Bowl LVI and the NFC North Division had more passing attempts,

passing touchdowns and passing yardage than the rest of the

division and league average. These two teams are winners in their

respective places.

â— Therefore, it seems that there is a positive correlation between

placing more attention and resources to the passing game than any

other offensive strategy if a team wants to win in the NFL.

â— As a result, the Chicago Bears will select a wide receiver with the

39th pick in the 2023 NFL draft.

Works Cited

FOX Sports. (2022). Chicago bears team game log – NFL. Chicago Bears Team Game Log – NFL | FOX

Sports. Retrieved July 25, 2022, from https://www.foxsports.com/nï¬‚/chicago-bears-team-game-log

National Football League. (2022). Ofï¬cial site of the National Football League. NFL.com. Retrieved July

25, 2022, from https://www.nï¬‚.com/stats/team-stats/offense/rushing/2021/reg/all

Pro Football Reference. (2022). NFL season by Season Team Offense. Pro Football Reference. Retrieved

July 25, 2022, from https://www.pro-football-reference.com/years/NFL/index.htm

Team Rankings. (2022). NFL team opponent time of Possession Percentage (excluding OT). NFL Football

Stats – NFL Team Opponent Time of Possession Percentage (Excluding OT) | TeamRankings.com.

Retrieved July 25, 2022, from

https://www.teamrankings.com/nï¬‚/stat/opponent-time-of-possession-pct-net-of-ot

Problem: Quantitative and Qualitative Variables

Stock Market

1. QUANTITATIVE VARIABLES

According to numerous sources, the stock market’s liquidity is crucial because it promotes control

of savings for the lifetime of investments, enabling investors to keep access to their assets for

the duration of the investment. When investors want to change their portfolios, the stock

market’s high liquidity “allows savers to purchase and sell promptly and affordably.” The stock

market’s increased liquidity makes long-term investments more accessible. The liquidity

parameter also has a favorable effect on economic growth by increasing capital’s marginal

productivity. Furthermore, it is asserted that liquidity encourages long-term investment,

information acquisition, and investments. Behavior revealed the harmful effects of increased

market liquidity. Reduction of saving rate and the need for precautionary saving as a result of an

increase in investment return, which may or may not have an impact on economic growth.

Investors may start funding high-return ventures like in response to increased risk diversification.

The savings rate can drop by eliminating risk diversification through an integrated stock market

at the global level, which would therefore reduce economic development and welfare. The

allocation of resources is improved and economic growth is accelerated by risk diversification.

Risk diversification can be quantified using the multifactor international arbitrage pricing model,

which is used to measure stock market integration, or by comparing the size of transactional

equity to the size of the economy using the total value of shares traded on the stock market to

GDP, also known as the stock market total value trading ratio (STR).

Investors assess a company’s financial soundness using quantitative analysis. While some

investors prefer to use only one research technique to assess long-term investments, it is best to

employ a mix of fundamental, technical, and quantitative analysis. The advent of the computer

era led to the development of quantitative analysis, which made it simpler than ever before to

evaluate vast volumes of data quickly. Quantitative trading analysts (quants) recognize trade

patterns, create models to evaluate those patterns, and then utilize the data to forecast the price

and direction of assets. Quants use the data to set up automatic trades of securities after the

models have been created and the information has been acquired. Comparative analysis, which

looks at things like a company’s structure, the composition of its management team, and its

strengths and shortcomings, is different from quantitative analysis.

A specialized trader known as a “quant” uses quantitative and mathematical approaches to

assess financial products or markets. They can assess risks and locate trading opportunities in

this manner. To find trading opportunities and to buy and sell stocks, they employ mathematical

models. The profession has become quite competitive due to the surge of applicants from

academia, software development, and engineering. Quantitative analysis, as opposed to

qualitative analysis, enables a reduced risk procedure through its dispassionate, objective,

numbers-based approach to determining whether or not a financial asset on the stock market is

useful for investors. Investors can use quantitative analysis to help them make investment

decisions from anywhere in the world and for less money than they would pay an expensive

analyst team. Additionally, investors can manage their portfolios in real time, make decisions,

and save time, money, and effort by using the tools while delegating the labor-intensive tasks to

the algorithms.

1. Statistics Exams

I tested for stationary using the Augmented Dickey-Fuller Unit to prevent the biased results

caused by the probable existence of unit roots in our variables. The Johansen-Juselius co

integration approach was used to assess the long-run equilibrium between variables and

determine whether the time series under examination “have a similar stochastic model. In

order to determine whether there is non-randomness in the data, we also performed the

multicollinearity test. Using the min-max approach of linear scaling, parameters were

normalized.

2. Regression

Human capital as in, DEPTH as the size of the financial system as in relation to the size of the

economy as in, and investments as in were used as the control variables. The net secondary

school enrolment ratio will be used to gauge human capital. Investments will be assessed as

real GDP-related investments, while DEPTH is a measure of liquid liabilities plus demand and

interest for bearing liabilities. With growth as the dependent variable and liquidity, size, risk

diversification, openness, and volatility as the independent variables, we conducted a

multiple regression using ordinary least squares.

3. Visualization of data

Individual graphs will be shown in the paragraph that follows to emphasize the data utilized

in the regression. For Germany, France, Luxembourg, and the United States of America,

graphs show annual percentage changes in the size, risk diversification, and openness of the

national stock market as well as economic growth. Economic development and stock market

openness over the studied period followed a similar pattern in all of the countries. The size

of the stock market and economic expansion also exhibit a similar trend, however the

declines had a bigger impact on economic growth than the trend’s upward movement. Even

more intriguing is the one-year lag of the annual variations between the openness of

economic expansion and the stock market.

4. Quantâ€™s construct their equations using a number of data sources.

Investors can choose whether to invest in a specific asset based on historical investment data,

stock market information, ratios, and cash flow valuations. The trading algorithms and

computer models make unbiased inferences from this data. Since everything is automated,

there is less chance of making rash decisions based on emotional upheaval. Stable algorithms

continuously evaluate each potential investment using the same data sets, such as the priceto-earnings ratio or discounted cash flow valuations. There is significantly less chance of

making hasty or foolish decisions because patterns and numbers are the only determining

factors.

QUANTITATIVE STRATEGIES:

Momentum Techniques:

It is important to note that momentum can also exist within a day, even if we categorize it with

a broader time period than a day. For lengthier time frames as well as during the day, traders can

find momentum. The strategies that we examine in the section below have long-term

momentum. A momentum strategy is focused on spotting and adhering to a market price trend.

It is predicated on the idea that an asset’s price will move steadily in one direction until the

strength of the price trend wanes. The trade volume and pace of price change are used to

calculate the momentum. Time-series momentum and cross-sectional momentum are two

different types of momentum.

Temporal Momentum

Time series momentum denotes a correlation between past and present returns. Researchers

use a statistical method to calculate the correlation coefficient of the returns in order to create

a time-series momentum strategy, where the null hypothesis denotes no correlation between

returns. The correlation coefficient of returns might fluctuate between lags, and occasionally the

strongest correlation is found between returns at various lags.

Momentum across Sections

Cross-sectional momentum is based on how two price series compare to one another when it

comes to performance, with one price series outperforming the other. In this kind of strategy

support, the underlying premise is that if one price series outperforms another in the present, it

will probably continue to do so in the future.

Utilizing Momentum Strategies

Technical indications and breakouts can serve as the foundation for momentum strategies. If the

price exceeds the upper Bollinger band, the N-day Moving Average, the Exponential Moving

Average, or a new N-day high, for instance, it may be reasonable to establish an entry signal to

purchase.

2. QUALITATIVE VARIABLES

We may deduce from the qualitative approach that the stock market could promote growth in a

very complicated manner, built on a web of economic levers and systems. The procedure is twoway and built on a multivalent logic, making it capable of more intricate structural

transformations. The qualitative approach led to the following inferences: the elements are

arranged in three-field conglomerations; conglomerations have the ability to permute circularly

three letters at a time; and the three fundamental elements are built using the auto orphisms

covered in this work. The qualitative model is equally viable, however it faces competition from

a number of variables.

While endogenous growth models develop their relationship through determination functions,

the relationship within the hexagonal fractalization system is performed through feedback loops

and commutative diagrams, allowing for self-stimulation and self-inhibition of the system as well

as the formation of accumulation zones. Complex interactions, such as those of interrelationship

between various systems, develop inside the fractalized system (initially formed in a hyper cubic

dimension). This makes it possible to monitor the impacts of changing one parameter, even if

those effects take place in a different system. The automation team is enduring as it is preserved

and the cascade was self-initiated and self-sustaining. A relational structure is both sustainable

and unsustainable, and while building the model, it produces economic growth in a qualitative

way.

Quants don’t visit companies, meet the management teams, or examine the items the companies

sell in order to identify a competitive edge, in contrast to typical qualitative investment analysts.

They frequently are unaware of or uninterested in the qualitative characteristics of the

businesses they invest in or the goods or services these businesses offer. Instead, they just use

numbers to determine which investments to make.

In qualitative analysis, judgments are made based on “soft” or immeasurable data. Qualitative

analysis deals with elusive and imprecise data that can be challenging to gather and quantify.

Since intangibles cannot be quantified by numerical numbers, machines find it difficult to

perform qualitative analysis. The foundation of qualitative analysis is an understanding of people

and organizational cultures. Qualitative analysis is aided by knowing a company’s competitive

advantage and viewing it through the eyes of its customers.

1. Corporate responsibility:

Compared to the more straightforward subject of management integrity, corporate governance

has a much wider definition. Corporate governance essentially assesses whether the company’s

senior management is acting in the best interests of the shareholders, particularly the minority

shareholders. A lot depends on factors like disclosure, transparency, management ideals, and

consistency. Companies have recently suffered severe losses as a result of poor corporate

governance. Look at a few examples. After revelations of instances of secretive intra-group

transactions. After the auditors raised concerns and ultimately left under dubious circumstances,

stocks.

2. Resilience of the business model:

Although it is difficult to put a number on this, we can use an example to demonstrate. Let’s

consider what happened to Nokia as an illustration. People regarded the Nokia Symbian

operating system lacking when Apple released the smart phones in 2007. The Nokia business

strategy was built on the premise that phones would remain voice-only devices and that data

would continue to favor PCs and laptops. That was a serious error of judgment. Consumer

preference quickly switched over the following few years to data use on mobile devices, where

operating systems like Apple IOS and Google Android went on to gain dominance. Simply enough,

Nokia’s business model was insufficiently sound. Consider whether the company’s business

model is reliable enough. Consider the Indian pharmaceutical sector. For 25 years, these

pharmaceutical corporations paid little attention to intellectual property (IP) and concentrated

on using reverse engineering to make generic drugs at a considerably lower price. The majority

of pharmaceutical businesses began to lose value and profits as low-cost nation competition

increased and US consumers became more demanding. Simply put, the business strategy wasn’t

strong enough.

Fundamental Analysis

Qualitative Factors:

Quantitative Factors:

~ BBussiness Model

~ Industry Growth

~ Competitive Advantage

~ Competition

~ Management

~ Customers

~ Corporate Governance

~ Fnancial statement

Quantitative Data

Mean

Median

Mode

YES

YES

YES

YES: Can be computed

NO: Cannot be computed

Quantitative Data

Ordinal

NO

YES

YES

Quantitative Data

Nominal

NO

YES

YES

Boston Housing Data

Analysis

Aniketh Reddy Jakkidi

Overview

âž¢ Real estate market has been expanding over the last 30 years.

New Housing tracts are planned to be constructed in Boston.

Numerous factors like residential land, accessibility to highways

and employment centres, number of rooms effect the price of

Houses in the housing tracts.

âž¢ The goal is to predict the median house price in new tracts based

on information such as crime rate, pollution, and number of

rooms.

Data Source

âž¢ Boston Housing dataset is taken from Kaggle. Dataset contains 506 observations and 14 variables.

âž¢ The Boston Housing Dataset is a derived from information collected by the U.S. Census Service concerning

housing in the area of Boston. The following describes the dataset columns:

CRIM – per capita crime rate by town

ZN – proportion of residential land zoned for lots over 25,000 sq. ft.

INDUS – proportion of non-retail business acres per town.

CHAS – Charles River dummy variable (1 if tract bounds river; 0 otherwise)

NOX – nitric oxides concentration (parts per 10 million)

RM – average number of rooms per dwelling

AGE – proportion of owner-occupied units built prior to 1940

DIS – weighted distances to five Boston employment centres

RAD – index of accessibility to radial highways

TAX – full-value property-tax rate per $10,000

PTRATIO – pupil-teacher ratio by town

B – 1000(Bk – 0.63)^2 where Bk is the proportion of blacks by town

LSTAT – % lower status of the population

MEDV – Median value of owner-occupied homes in $1000’s

âž¢ Data Source Link: Boston housing dataset | Kaggle

Histogram

Distribution of House

price is right skewed,

with most housing

tracts has median value

of 18,000$ to 24,000$.

Box Plot

Housing tracts near to

boundaries of Charles river

has high median value

compared housing tracts

far from Charles river.

Box Plot

Housing tracts near to

highways has higher prices

compared to housing tracts

from highways. But we can

see outliers due to other

factors impact on Median

housing price of tracts

Box Plot

Higher category Housing

tracts have lower pupilteacher ratio, lower

pollution, lower

industrialized and less

people with lower

status

Scatter Plot

As people with lower

status of population

increases median value

of housing tracts

decreases.

Scatter Plot

Overall pollution and

median value of

housing tract has

negative correlation

but the amount is

not significant

Descriptive Statistics

Descriptive Statistics like

mean, median, mode,

range, skewness, IQR are

shown. MEDV has mean

value of 22.533 and

skewness of 5.22

Balance of Data set

This data set has

83.4% of lower

median value

housing tracts and

only 16.6% of

higher median

value housing

tracts

Outliers

Based on Grubbâ€™s Test at

95% confidence level

there are no outliers of

MEDV value in the Boston

housing data set

Correlation and Cross Tabulation

MEDV value has highest

correlation with RM (average

number of rooms per

dwelling). As RM increases

MEDV increases.

MEDV has has negative

correlation with LSTAT,

PTRATIO and INDUS

Conclusion

âž¢ Median Housing tract prices depend mostly on average number

of rooms per dwelling, pupil-teacher ratio by town, full-value

property-tax rate and per capita crime rate by town.

âž¢ From all visualizations, price has positive relationship with

average number of rooms per dwelling and strong negative

relationship with LSTAT(% lower status of the population)

âž¢ The amount of variation in median value of housing tract due to

each predictor can be further explored using data modelling.

THANK YOU

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